Source Apportionment of Fine Aerosol at an Urban Site of Beijing

21 22 Fine particles were sampled from 9 November to 11 December 2016 and 22 May 23 to 24 June 2017 as part of the Atmospheric Pollution and Human Health in a Chinese 24 megacity (APHH-China) field campaigns in urban Beijing, China. Inorganic ions, trace 25 elements, OC, EC, and organic compounds including biomarkers, hopanes, PAHs, n26 alkanes and fatty acids, were determined for source apportionment in this study. 27 Carbonaceous components contributed on average 47.2% and 35.2% of total 28 reconstructed PM2.5 during the winter and summer campaigns, respectively. Secondary 29 inorganic ions (sulfate, nitrate, ammonium; SNA) accounted for 35.0% and 45.2% of 30 total PM2.5 in winter and summer. Other components including inorganic ions (K , Na, 31 Cl), geological minerals, and trace metals only contributed 13.2% and 12.4% of PM2.5 32 during the winter and summer campaigns. Fine OC was explained by seven primary 33 sources (industrial/residential coal burning, biomass burning, gasoline/diesel vehicles, 34 cooking and vegetative detritus) based on a chemical mass balance (CMB) receptor 35 model. It explained an average of 75.7% and 56.1% of fine OC in winter and summer, 36 respectively. Other (unexplained) OC was compared with the secondary OC (SOC) 37 estimated by the EC-tracer method, with correlation coefficients (R) of 0.58 and 0.73, 38 and slopes of 1.16 and 0.80 in winter and summer, respectively. This suggests that the 39 unexplained OC by CMB was mostly associated with SOC. PM2.5 apportioned by CMB 40 showed that the SNA and secondary organic matter were the highest two contributors 41 to PM2.5. After these, coal combustion and biomass burning were also significant 42 sources of PM2.5 in winter. The CMB results were also compared with results from 43 Positive Matrix Factorization (PMF) analysis of co-located Aerosol Mass Spectrometer 44 (AMS) data. The CMB was found to resolve more primary OA sources than AMS-PMF 45 but the latter apportioned more secondary OA sources. The AMS-PMF results for major 46 components, such as coal combustion OC and oxidized OC correlated well with the 47 results from CMB. However, discrepancies and poor agreements were found for other 48 OC sources, such as biomass burning and cooking, some of which were not identified 49 in AMS-PMF factors. 50 https://doi.org/10.5194/acp-2020-1020 Preprint. Discussion started: 3 December 2020 c © Author(s) 2020. CC BY 4.0 License.


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The composition of PM2.5 applying the chemical mass closure method is plotted in Fig.2 290 and summarized in Table S1. Because the gravimetrically measured mass (offline PM2.5) 291 differs slightly from online PM2.5 (Fig. S2), the regression analysis results between mass 292 reconstructed using mass closure (reconstructed PM2.5) and both measured PM2.5 293 (offline PM2.5/ online PM2.5) were investigated and plotted in Fig. 3.   Bound water contributed 4.6% and 7.2% of PM2.5 during the winter and summer, 321 respectively. All other components combined accounted for 13.2% and 12.4% of PM2.5 322 during the winter and summer campaigns, respectively.      The correlation coefficient (R 2 ) between OC-CC and Clduring winter was 0.62 but 374 there is no significant correlation between the two during the summer campaign while.

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This is probably related to the semi-volatility of ammonium chloride, which is liable to 376 evaporate in summer (Pio and Harrison, 1987). A similar phenomenon has been

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Cooking is expected to be an important contributor of fine OC in densily populated 438 Beijing, which has a population of over 21 million. The cooking source profile was 439 selected from a study which was carried out in the urban area of another Chinese

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The Other OC was calculated by subtracting the calculated OC (the sum of OC from 457 seven main sources) from measured OC concentrations. As shown in Table S2, there   Table   507 1. Daily concentrations of Other OC estimated by CMB and SOC estimated by the EC-508 tracer method in winter and summer are plotted in Fig. 6, as well as their correlation 509 relationship.  but not on identical days. The comparison results are presented in Table 3.

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As shown in Table 3, slightly more OC was explained by CMB at the urban site observations. Cooking accounted for over 10% of OC at the urban site, but less than 5% 551 at the rural site, which is plausible as the urban site is more densely populated.

555
Results from AMS-PMF were compared with the CMB source apportionment results 556 to investigate the consistency and potential uncertainties of both methods, and also to  Table 4.   results is plotted in Fig. 8. A similar temporal trend was found between them, especially 616 in summer, which was also observed with a better correlation (R 2 =0.73).  The source contributions to PM2.5 were calculated by multiplication of the fine OC 632 source estimates from CMB by the ratios of fine OC to PM2.5 mass (Table S3) Table S4. 648 As shown in Table S4, PM2.5 mass was well explained by those sources which 649 accounted for 91.9±24.1% and 99.0±19.1% of online PM2.5 in winter and summer, 650 respectively. In the summer, the offline PM2.5 is lower than online observations. Thus, 651 the CMB-based source contributions are more than offline PM2.5 mass (121.7±26.6%).